Systems and methods for seeding network determinations using network profile data

Abstract

Systems and methods use non-natural language embeddings to generate natural language output with improved precision using a classification seed. For example, a system can initiate a first conversation between a user and a first application, where the first conversation includes a first input. The system can generate a seed based on user profile data aggregated prior to initiating the first conversation. The system can seed a first artificial intelligence model with the seed to generate a seeded artificial intelligence model. The system can input the first input into the seeded artificial intelligence model to generate a first output. The system can generate a first recommendation from the seeded artificial intelligence model based on the first output for display on a user interface.

Description

Technical Field

[0001] This disclosure relates to the field of artificial intelligence (AI), and more specifically to systems and methods for using web profile data to seed web determinations. Background Technology

[0002] In recent years, the use of artificial intelligence (AI) has grown exponentially, including but not limited to machine learning and deep learning (collectively referred to in this paper as AI models, machine learning models, or simply models). Broadly speaking, AI refers to a wide branch of computer science that involves building intelligent machines capable of performing tasks that typically require human intelligence. A key benefit of AI is its ability to process data, find underlying patterns, and / or perform real-time determination. However, despite these benefits and a vast number of potential applications, the practical implementation of AI is hampered by several technical challenges. First, AI may rely on large amounts of high-quality data. Obtaining this data and ensuring its high quality can be complex and time-consuming. Furthermore, the acquired data may need to be accurately classified and labeled, which can be a difficult, time-consuming, and manual task. Second, even with AI models trained on large amounts of high-quality data, the inputs to a given task (e.g., interpreting a user's intent) are often similar (e.g., using the same words), thus producing the same output even if the user has distinctly different intents. This inherent limitation of AI models—the inability to distinguish between different intents when receiving similar inputs—poses a fundamental problem for attempting to use AI-based solutions in short messaging and / or dynamic response applications (e.g., chatbots). Summary of the Invention

[0003] This paper describes novel uses and / or improvements to systems and methods for seeding artificial intelligence applications. As an example, this paper describes systems and methods for overcoming the inability of AI models to generate more accurate outputs when inputs are substantially the same. These systems and methods achieve this by using more robust mechanisms to differentiate inputs. More specifically, these systems and methods can seed network determinations (e.g., recommendation, route determination, dynamic response, etc.) using network profile data, user profile data, and / or natural language input.

[0004] For example, using natural language input (e.g., network routing data, device information, and / or other raw text) into a model is technically more limiting than using embeddings due to the inherent complexity and ambiguity associated with natural language processing. When natural language input is provided, the system must match it with specific model tokens, which can be restrictive because each text input can only be associated with one set of tokens. This tokenization process can lead to the loss of subtle meanings because each word or phrase in the text must be represented by discrete tokens, which may not fully capture subtle changes in context or meaning. In contrast, embeddings provide a more complex and precise text representation. Embeddings are continuous vector representations that encapsulate semantic information from text, allowing models to work with richer and more variable inputs. This reduces the need to rely on exact words and their direct token matching, thus circumventing the limitations of token ambiguity and strict text matching. By leveraging embeddings, models can better understand and process the latent meaning of inputs, resulting in more accurate and flexible determination and prediction.

[0005] However, the use of embeddings does introduce new technical challenges when used as input to generate real-time output. For example, while embeddings are powerful in capturing semantic information and reducing ambiguity, they abstract the actual words and phrases of the input text. This abstraction means the model operates at a level detached from the specific linguistic structures and vocabulary of natural language. When generating output, the model must translate these dense, multi-dimensional vector representations back into coherent and context-aware language. This translation process is highly complex because embeddings do not preserve the syntactic and grammatical details inherent in natural language input. Consequently, the model may struggle to reconstruct the precise phrasing and fluency required for natural language generation. Furthermore, embeddings may lose fine-grained distinctions between words that are crucial for producing accurate and context-aware responses. Thus, while embeddings enhance the model's understanding of semantic content, they introduce a layer of complexity in translating this understanding back into natural, human-readable text. This complexity makes embeddings difficult (e.g., in terms of time and processing requirements) to use in real-time applications such as network routing and / or dynamic response applications (e.g., chatbots).

[0006] To overcome the novel technical challenges that arise when using embeddings as input to generate natural language input, the system and method instead seed the model with embeddings. Seeding the model with embeddings differs from using embeddings as input in terms of the stage and purpose of their application within the model's processing pipeline. When an embedding is used as input, it directly replaces the original text input, serving as the starting point for model operations. This means that the model's layers and transformations begin with these dense, continuous vector representations, bypassing the need for initial tokenization and embedding generation. This approach simplifies certain processes but also requires the model to be adept at handling and interpreting these preprocessed embeddings from the outset (e.g., a challenging task for short message sending and / or dynamic response applications).

[0007] On the other hand, seeding a model with embeddings refers to initializing or providing context for the model's subsequent operations. This seeding sets the stage for the model's behavior by embedding specific initial states or contextual information, thus influencing how the model processes subsequent inputs or generates outputs. Compared to using embeddings as direct input, seeding integrates these embeddings at a more strategic location in the model workflow, leveraging their semantic depth to shape the model's performance and output without completely replacing the initial text input stage. This approach allows for the combination of preprocessed semantic information and real-time natural language processing, thereby enhancing the model's ability to produce context-rich and coherent language.

[0008] Unfortunately, seeding a model introduces another technical problem, particularly overseeding, which can lead to the same issues as using embeddings as input (e.g., excessive complexity). Restricting the embeddings used for seeding a model to only a limited number of categories or predefined types reduces the complexity the model faces when generating outputs by narrowing the scope of input information. By focusing on a predefined set of categories, the model can operate in a more structured and controlled environment. This restriction simplifies the range of possible interpretations and responses the model must consider, thereby enhancing its ability to generate accurate and relevant outputs. The model does not need to process and distinguish a large number of different semantic vectors, which can be computationally intensive and prone to ambiguity. Instead, it can focus on a smaller, well-defined set of embeddings, thus streamlining the generation process.

[0009] Furthermore, using category designation for each seed helps prevent the model from having to decipher information from different categories. When embeddings are categorized, the model can easily identify and process information based on these predefined labels. This categorization acts as a guiding framework, ensuring that the model can quickly and accurately align the input embeddings with the appropriate context and semantic information needed to generate the output. It minimizes the risk of cross-category confusion, which the model might otherwise struggle to separate and correctly interpret overlapping or intertwined information from various categories. By clearly delineating categories, the model can maintain a clear and organized representation of the input data, resulting in more coherent and context-aware output.

[0010] In some aspects, systems and methods are described for generating natural language output with improved accuracy using non-natural language embeddings and classification seeding. For example, the system may initiate a first session between a user and a first application, where the first session includes a first input. The system may generate a seed based on user profile data aggregated prior to initiating the first session. The system may seed a first artificial intelligence model with the seed to generate a seeded artificial intelligence model. The system may input the first input into the seeded artificial intelligence model to generate a first output. The system may generate a first recommendation from the seeded artificial intelligence model based on the first output for display on a user interface.

[0011] Various other aspects, features, and advantages of the invention will become apparent from the detailed description and accompanying drawings. It should also be understood that the foregoing general description and the following detailed description are exemplary and do not limit the scope of the invention. As used in the specification and claims, the singular forms “a,” “an,” and “the” include plural indicators unless the context clearly specifies otherwise. Additionally, as used in the specification and claims, the term “or” means “and / or” unless the context clearly specifies otherwise. Furthermore, as used in the specification, “a portion” means a part or all (i.e., the entire portion) of a given item (e.g., data) unless the context clearly specifies otherwise. Attached Figure Description

[0012] Figure 1A A schematic diagram illustrating an explanation of generating network route predictions according to one or more embodiments is shown.

[0013] Figure 1B An illustrative user interface is shown, according to one or more embodiments, for generating an interpretation of a prediction used to determine a dynamic dialogue response.

[0014] Figure 2A A schematic diagram is shown, according to one or more embodiments, for generating explanations related to cybersecurity predictions.

[0015] Figure 2B A schematic diagram is shown, according to one or more embodiments, for generating explanations related to the generated recommendations.

[0016] Figure 3 A schematic component of a system for seeding an artificial intelligence model is shown according to one or more embodiments.

[0017] Figure 4 A flowchart illustrating the steps involved in using an artificial intelligence model for seeding according to one or more embodiments is shown. Detailed Implementation

[0018] In the following description, numerous specific details are set forth for purposes of explanation in order to provide a thorough understanding of embodiments of the invention. However, those skilled in the art will understand that embodiments of the invention can be practiced without these specific details or with equivalent arrangements. In other instances, well-known structures and devices are illustrated in block diagram form to avoid unnecessarily obscuring embodiments of the invention.

[0019] Instead, the system and method seed the model with embeddings. This seeding sets the groundwork for the model's behavior by embedding specific initial states or contextual information, thus influencing how the model processes subsequent inputs or generates outputs. Compared to using embeddings as direct input, seeding integrates these embeddings at a more strategic location in the model workflow, leveraging their semantic depth to shape the model's performance and output without completely replacing the initial text input stage. This approach allows for the combination of preprocessed semantic information and real-time natural language processing, thereby enhancing the model's ability to produce context-rich and coherent language.

[0020] Unfortunately, overseeding a model raises the same technical problems as using embeddings as input (e.g., excessive complexity). Restricting the embeddings used for seeding a model to only a limited number of categories or predefined types reduces the complexity the model faces when generating outputs by narrowing the scope of input information. By focusing on a predefined set of categories, the model can operate in a more structured and controlled environment. This restriction simplifies the range of possible interpretations and responses the model must consider, thereby enhancing its ability to generate accurate and relevant outputs. The model does not need to process and distinguish a large number of different semantic vectors, which can be computationally intensive and prone to ambiguity. Instead, it can focus on a smaller, well-defined set of embeddings, thus streamlining the generation process.

[0021] Therefore, systems and methods need to interpret embeddings. Interpretability, or explainability, is a significant challenge in seeding artificial intelligence (AI) models because many advanced models, particularly deep learning networks, operate as “black boxes” with complex, non-linear decision-making processes that are difficult to dissect and understand. As AI systems become increasingly complex, they learn patterns from vast amounts of data without explicitly revealing how they arrive at their conclusions. This opacity makes it difficult to validate model reliability, detect biases, ensure ethical compliance, and build user trust. In critical applications such as healthcare, finance, and autonomous systems, the inability to interpret AI decisions can have serious consequences, making transparency crucial for accountability and regulatory compliance. Furthermore, without explicit explanation, debugging and improving AI models becomes more challenging and can hinder innovation and responsible deployment. Addressing interpretability requires developing techniques that balance model complexity with transparency to ensure that AI systems remain robust and understandable.

[0022] This paper further describes novel uses and / or improved systems and methods for artificial intelligence applications, particularly regarding the interpretability of AI applications and / or the predictions provided by those applications. As an example, this paper describes systems and methods for enhanced interpretability in network routing decisions. In this way, the systems and methods described in this paper can allow for the use of network embedding features to describe the underlying principles of prediction.

[0023] Existing systems can leverage embeddings from large deep learning models in downstream applications to enhance predictions by utilizing these embeddings as additional features alongside traditional tabular features. Embeddings are dense, low-dimensional representations of data (such as words or items) generated by deep learning models trained on large datasets. These embeddings capture complex relationships and semantic meanings that are often not apparent in the original data. In downstream applications, embeddings can be integrated with traditional tabular features (such as numbers or categorical data) to provide a richer and more informative feature set for predictive models. This integration typically involves concatenating embeddings with tabular features to form a composite feature vector that combines the strengths of both types of data. Machine learning algorithms, such as gradient boosting machines or neural networks, can then be trained on these enhanced feature vectors for more accurate and robust predictions. By combining embeddings, models can capture underlying patterns and relationships in the data that might be missed when traditional features are used alone, thereby improving performance on various tasks such as recommendation systems, customer segmentation, and predictive analytics. This approach allows for a more nuanced understanding of the data and can significantly improve the predictive capabilities of AI systems in complex real-world applications. However, while embeddings offer benefits, these systems lack a mechanism for determining the meaning or definition of what each embedded feature represents. Consequently, embeddings cannot be used for any application requiring interpretability.

[0024] To overcome these technical limitations when using embeddings in AI models requiring interpretability, the system and method disclosed in this paper use auxiliary features that are defined and engineered to interpret the embedded features. For example, the defined / engineered features can be human-defined features. The system can then map these engineered features to the embedded features to obtain an agent that determines what the embedded features contain. Once the engineered features are defined, these definitions can then be used to interpret their corresponding embedded features. By doing so, embedded features can be leveraged as additional features alongside traditional tabular features to enhance predictions without negatively impacting the model's interpretability.

[0025] Because of this interpretability, the system can name each seed using a category, which helps prevent the model from having to decipher information from different categories. When embeddings are classified, the model can easily identify and process information based on these predefined labels. This classification acts as a guiding framework, ensuring that the model can quickly and accurately align the input embeddings with the appropriate context and semantic information needed to generate the output. It minimizes the risk of cross-category confusion, which the model might otherwise struggle to separate and correctly interpret overlapping or intertwined information from various categories. By clearly defining categories, the model can maintain a clear and organized representation of the input data, resulting in more coherent and context-aware output.

[0026] Figure 1A A schematic diagram illustrating an explanation of generating network route predictions according to one or more embodiments is shown. Figure 1A A system 100 is illustrated that can be used to determine network routes for data across network 104 from device 102 to device 106. Device 102 and / or device 106 may include any computing components. In some instances, device 102 may include a user interface. As mentioned herein, a “user interface” may include human-computer interaction and communication within a device and may include the appearance of a display screen, keyboard, mouse, and desktop. For example, a user interface may include how a user interacts with an application or website.

[0027] For example, seeding AI models for network routing can be highly beneficial because it enables dynamic, efficient, and adaptive decision-making in managing data traffic. Traditional routing algorithms often rely on static or predefined rules, which may not be optimal under changing network conditions such as congestion, failures, or fluctuating bandwidth availability. By leveraging AI, seeded models can continuously learn from historical and real-time data, predicting and adapting to network demands with minimal human intervention. This leads to improved traffic management, reduced latency, optimized resource allocation, and enhanced fault tolerance. Furthermore, AI-driven routing can detect and mitigate network anomalies or threats by identifying patterns that indicate malicious activity.

[0028] As mentioned in this article, "content" should be understood to mean electronically consumable user assets, such as internet content (e.g., streaming content, downloadable content, webcasts, etc.), video clips, audio, content information, images, rotated images, documents, playlists, websites, articles, books, ebooks, blogs, advertisements, chat sessions, social media content, applications, games, and / or any other media or multimedia and / or combinations thereof. Content can be recorded, played, displayed, or accessed by a user's device, but can also be part of a live performance. Furthermore, user-generated content can include content created and / or consumed by the user. For example, user-generated content can include content created by another person but consumed and / or published by the user.

[0029] Network 104 may include several essential components that work together to facilitate communication and resource sharing between devices. Key components include network devices such as routers, switches, and hubs, which manage and guide data flow across the network. Routers are responsible for forwarding data packets between different networks, ensuring information reaches its correct destination. Switches connect multiple devices within the same network, allowing them to communicate efficiently by routing data to the appropriate devices. Hubs, while less common today, also connect multiple devices, but broadcast data to all connected devices, resulting in less efficient communication. Additionally, network 104 may include end devices such as computers, servers, printers, and other peripheral devices that use the network to access and share resources. These devices are connected via network interfaces, which can be wired (using Ethernet cables) or wireless (using Wi-Fi). Network interfaces enable devices to communicate with each other and access the network infrastructure.

[0030] Network 104 can use communication protocols such as TCP / IP (Transmission Control Protocol / Internet Protocol) to define the rules and standards for data transmission. These protocols ensure that data is correctly formatted, sent, received, and processed, enabling seamless communication between different devices and networks. Network media, including cables (such as fiber optic, coaxial, and twisted-pair cables) and wireless signals, provide the physical means for data to travel. The choice of media affects the speed, distance, and reliability of data transmission within network 104. Network services (such as DNS (Domain Name System) and DHCP (Dynamic Host Configuration Protocol) support network functionality by translating domain names into IP addresses and dynamically assigning IP addresses to devices, respectively. These services ensure that devices can easily locate themselves on network 104 and communicate with each other.

[0031] System 100 can use models to perform network routing prediction, which optimizes characteristics such as bandwidth, throughput, power consumption, processing speed, reliability and other performance metrics by leveraging different machine learning training regimes (including supervised learning, unsupervised learning and reinforcement learning).

[0032] System 100 can use different training schemes depending on specific objectives and available data. In supervised learning, the model is trained on labeled data, where each training example includes input network conditions and the desired optimal routing decision. Historical network data, such as traffic patterns, node performance, and past routing decisions, serves as the primary source of training data. This data is labeled with performance metrics indicating the effectiveness of different routing strategies. The AI ​​model learns to map network conditions to optimal routing paths by minimizing the error between its predictions and the actual results observed in the training data. Supervised learning is particularly effective when a rich dataset of available historical performance metrics exists.

[0033] Unsupervised learning does not rely on labeled data. Instead, it aims to reveal hidden patterns and relationships within network data. For network routing, unsupervised learning can cluster network states or detect anomalies in traffic patterns. For example, clustering algorithms can group similar network conditions, helping to identify typical routing scenarios and their associated performance characteristics. This information can inform routing decisions by highlighting common traffic behaviors and potential bottlenecks. Training data for unsupervised learning is typically generated from historical network logs, capturing a wide variety of network conditions and traffic patterns without specific labels.

[0034] Reinforcement learning (RL) is particularly well-suited for dynamic and complex environments, such as network routing. In RL, an agent learns by interacting with the network environment, making routing decisions, and receiving feedback through rewards or penalties based on the resulting performance metrics. The agent aims to maximize cumulative rewards by learning the optimal routing policy over time. Training data for RL is generated through simulations or real-time interactive methods, where the agent explores various routing policies and observes their impact on network performance. Simulations can be used to create a wide range of scenarios, including rare or extreme conditions, ensuring the model can handle a broad spectrum of network states.

[0035] For supervised learning, training data is generated from historical network logs, where past routing decisions and their outcomes provide a labeled dataset. In unsupervised learning, the same historical logs are used, but the focus is on identifying patterns and clusters within the data, rather than specific labels. For reinforcement learning, training data is typically generated through a combination of real-time network interactions and simulated environments. Simulation allows RL agents to experience a wide range of network conditions, thus promoting robust learning without the risk of disrupting real-time network operation.

[0036] By combining these training methods, the model can develop a comprehensive understanding of the network and optimize routing decisions to enhance overall performance. Supervised learning provides a precise mapping from conditions to decisions, unsupervised learning reveals underlying patterns, and reinforcement learning dynamically adapts to real-time changes, ensuring the network operates efficiently and reliably.

[0037] Figure 1B A schematic user interface for generating an interpretation of predictions used to determine dynamic dialogue responses is illustrated according to one or more embodiments. For example, user interface 150 may receive one or more user queries (e.g., user query 152) and generate dynamic dialogue responses (e.g., response 172), as shown in user interface 170. Seeding AI models for chatbots can be highly beneficial because it enables more natural, intelligent, and context-aware interactions with users. By training on diverse datasets, seeded AI models can understand linguistic nuances, user intent, and dialogue patterns, resulting in more accurate and relevant responses. This enhances the user experience by providing personalized assistance, reducing response times, and ensuring consistent communication.

[0038] As described in this article, a user query can be a request for information or action made by a user to a system, typically through a search engine, database, or any interactive application such as a chatbot. A query represents a user's specific need or problem and can be expressed in various forms, such as keyword searches, natural language questions, or commands. In the context of search engines, a user query can be a set of keywords or a phrase entered into the search bar to find relevant information. For example, a user might type "best Italian restaurants in New York" to get a list of recommendations. In databases, user queries are typically in the form of a Structured Query Language (SQL) statement, designed to retrieve specific data from the database. For example, a query might be "SELECT * FROM employees WHERE department='Sales'". In interactive applications like chatbots, a user query can be a conversational prompt or question, such as "What's the weather like today?" or "Can you help me book a flight to London?" The chatbot processes the input to provide an appropriate response or perform the requested action.

[0039] As described in this article, dynamic conversational responses can be adaptive and context-sensitive replies generated in real time by a system such as a chatbot or virtual assistant based on specific input provided by the user. Unlike static responses that are pre-written and limited to specific prompts, dynamic responses can be generated on the fly and can flexibly and coherently handle a wide variety of user input. For example, if a user asks a chatbot, “Can you recommend some good restaurants nearby?”, a dynamic conversational response can take into account the user’s location, preferences, and possible previous interactions to suggest specific restaurants through personalized recommendations. If the user follows up with “What kind of cuisine do they offer?”, the chatbot will maintain the context and provide relevant information about the restaurant’s menu.

[0040] In some embodiments, the system can retrieve multiple recommendations and select a first recommendation from among them based on a first output, generating the first recommendation for display on a user interface. The system may collect a diverse set of potential recommendations stored in a recommendation database or repository. Each recommendation in this repository is associated with a specific criterion or condition specifying when it should be presented to the user. Once the system has generated an output, such as a prediction or classification result from a decision tree or another model, it uses that output to filter and evaluate the set of available recommendations. The system compares the output with the criteria associated with each recommendation. This involves checking whether the output meets the conditions or thresholds defined for each recommendation. For example, if the output indicates a high probability that the user is satisfied with a certain feature, the system might look for recommendations tailored to enhance the user experience or promote additional features. Conversely, if the output indicates potential dissatisfaction or a specific problem, the system will prioritize recommendations that address these problems, such as providing support or alternative solutions. After evaluating the recommendations, the system selects the recommendation that best aligns with the output. This selection process ensures that the selected recommendation is most relevant and beneficial to the user's current situation. The system then generates the selected recommendation in a format suitable for display on the user interface, creating a clear and actionable message that the user can easily understand and interact with. Finally, the system displays the selected recommendations on the user interface, ensuring their placement is prominent and user-friendly. Interactive elements (such as buttons or links) can be included to encourage immediate user action based on the recommendations.

[0041] In some embodiments, the system can generate a first recommendation for display on a user interface by retrieving a large language model and inputting a first output into it. The process begins by retrieving a pre-trained large language model, which has been trained on a broad dataset to understand and generate human-like text. Once the model is loaded, the system inputs the first output into the LLM. This first output (which can be a prediction, classification result, or any other analytical output from a previous process) serves as a prompt or context for generating recommendations. The large language model processes the input, leveraging its deep understanding of language patterns and context to formulate relevant and coherent recommendations. The LLM uses its embedded knowledge and the context provided by the first output to generate recommendations that align with user needs or system objectives. For example, if the first output indicates user preferences or detected problems, the LLM can formulate recommendations that enhance the user experience or effectively address the problem. Once the LLM generates a recommendation, the system formats it into a user-friendly message suitable for display. This involves ensuring the recommendation is clear, concise, and actionable, allowing the user to easily understand it and take action. The formatted recommendation is then displayed on the user interface, typically accompanied by interactive elements such as buttons or links that encourage further action. By using a large language model to generate recommendations based on the first output, the system can provide highly contextualized and nuanced suggestions, thereby improving user engagement and satisfaction.

[0042] For example, such as Figure 1B As shown, the system can determine the context of a user query (e.g., query 152) to determine a personalized response (e.g., response 172). In some embodiments, the system may use user profile information to generate response 172 (e.g., user profile information indicating account balance). The system may monitor user-generated content to generate user profile data. As mentioned herein, "user profile" and / or "user profile data" can include data about a user that is actively and / or passively collected. For example, user profile data can include user-generated content and user characteristics. User profiles can be content consumed and / or created by the user.

[0043] User profile data may also include user characteristics. As mentioned herein, "user characteristics" may include information about the user and / or information included in a catalog of stored user settings, preferences, and user information. For example, a user profile may have settings for the user's installed programs and operating system. In some embodiments, a user profile may be a visual display of personal data associated with a specific user or a customized desktop environment. In some embodiments, a user profile may be a digital representation of a person's identity. Data in user profiles may be generated based on active or passive monitoring of the system.

[0044] In some embodiments, the system can seed an artificial intelligence model with user profile data to generate better recommendations by collecting and analyzing relevant user attributes, preferences, and behavioral patterns. This process may involve collecting data such as browsing history, purchase records, interactions, demographic details, and explicit user input, which serves as the basis for the seeded model.

[0045] The system can use a model that employs Natural Language Processing (NLP) techniques and machine learning algorithms to generate dynamic conversational responses in chatbot applications. This model (typically based on a neural network architecture, such as a transformer model (e.g., GPT-4)) can be designed to understand and generate human-like text based on the context of the conversation. The model can process input from the user, identify intent, and formulate coherent and context-aware responses. This process involves several stages, including tokenization (breaking text into smaller units), understanding semantic meaning, and generating relevant responses to maintain the flow of the conversation.

[0046] Various approaches can be used to train models for this application, including supervised learning, unsupervised learning, and reinforcement learning (as described above). In supervised learning, the model is trained on a large corpus of labeled dialogue data, where each input is paired with a desired response. This data typically comes from transcriptions of real dialogues, chat logs, or specially curated datasets covering a wide range of dialogue contexts. The model learns to predict appropriate responses based on the input text, gradually improving its accuracy by minimizing the discrepancy between its predictions and actual responses in the training data.

[0047] On the other hand, unsupervised learning does not rely on labeled data. Instead, it involves training a model on large amounts of text data without explicit input-response pairs. The model learns the structure and patterns of natural language through techniques such as language modeling, where it predicts the next word or sequence of words in a sentence. This approach helps the model understand grammar, context, and common dialogue patterns, making it better at generating seemingly plausible responses even in unfamiliar contexts.

[0048] Reinforcement learning can also be applied, where the model interacts with the user in real time and receives feedback on the quality of its responses. This feedback serves as a reward or penalty, guiding the model to improve its dialogue strategies. For example, positive feedback (such as user satisfaction or engagement metrics) encourages the model to generate similar responses in the future, while negative feedback helps it avoid ineffective or inappropriate responses. This approach allows the model to continuously adapt and improve based on real-world user interactions.

[0049] To generate training data using these different mechanisms, a combination of real-world dialogue data, simulated interactions, and data augmentation techniques is typically used. Real-world dialogue data provides authentic examples of human conversation, while simulated interactions create diverse scenarios to expose the model to various conversational contexts. Data augmentation techniques, such as paraphrasing and introducing language variations, further enrich the training dataset, ensuring the model learns to handle a wide range of inputs. By integrating these training schemes, the AI ​​model develops a robust understanding of natural language, enabling it to generate dynamic and context-aware dialogue responses in chatbot applications. This adaptability ensures that the chatbot can effectively engage with users, providing relevant and meaningful interactions across different conversational scenarios.

[0050] Figure 2A A schematic diagram is shown, illustrating a method for generating explanations related to cybersecurity predictions, according to one or more embodiments. For example, such as... Figure 2A As shown, system 200 can detect one or more cybersecurity incidents based on the actions of one or more users (e.g., user 202). For example, the system can seed an artificial intelligence model with data related to cybersecurity incidents by collecting and analyzing historical and real-time security events, threat intelligence, and attack patterns. This data may include logs from intrusion detection systems, firewall alerts, malware signatures, phishing reports, and user behavior analysis. By processing and constructing this information, the AI ​​model learns to identify anomalies, correlate attack vectors, and classify incidents based on severity and type.

[0051] In some examples, cybersecurity incidents that can be detected using the model can include a wide range of malicious activities and anomalies. These can include phishing attacks, where the model can identify suspicious emails attempting to trick employees into revealing sensitive information. The model can also detect malware by identifying anomalous patterns in behavior or code within software applications. Furthermore, the model can identify brute-force attacks by detecting numerous failed login attempts within a short period. Advanced persistent threats (APTs) involving long-term and targeted cyberattacks can also be detected by the model by monitoring subtle and persistent anomalies in network traffic and system behavior. For example, the model can (e.g., at server 204) receive user activity and analyze data on anomalous network traffic and system behavior.

[0052] This model can analyze massive amounts of data in real time, making it particularly effective at identifying these and other threats, such as data breaches, insider threats, and distributed denial-of-service (DDoS) attacks, by recognizing patterns that deviate from norms. In some embodiments, the system can detect cybersecurity incidents at financial services companies. A common threat is fraud, where attackers use stolen credentials or synthetic identities to conduct unauthorized transactions or access sensitive financial information. Phishing schemes are also prevalent, targeting employees and customers to steal personal information or distribute malware. Furthermore, ransomware attacks pose a significant risk, where malware encrypts company data and demands ransom for its release. Financial institutions are also vulnerable to sophisticated attacks, such as APTs, where cybercriminals infiltrate networks undetected for extended periods to extract valuable data. Insider threats, whether malicious or accidental, can also lead to serious security breaches. Additionally, DDoS attacks can overwhelm company servers with massive amounts of traffic, causing service disruptions, downtime, and potential financial losses. The AI ​​model helps mitigate these risks by continuously monitoring anomalous activity, analyzing large amounts of threat data, and providing early warnings of potential security incidents, thereby enhancing the company's overall security posture. By analyzing patterns in user activity, the system can generate one or more recommendations that can be displayed on user device 206.

[0053] Figure 2B A schematic diagram is shown for generating explanations related to the generated recommendations, according to one or more embodiments. For example, system 250 can use model 254 to generate recommendations based on user activity to generate recommendations on a user device (e.g., device 256).

[0054] System 250 can generate recommendations by leveraging algorithms and data analytics techniques to suggest items, services, or content that align with a user's preferences and behaviors. This process typically begins with data collection, where the system gathers information about user interactions (e.g., data 252), such as browsing history, purchase history, location, merchant information, ratings, and / or demographic data. This data is then processed and analyzed using various recommendation algorithms, which can be broadly categorized as collaborative filtering, content-based filtering, and hybrid methods.

[0055] System 250 can use collaborative filtering to determine that users who have exhibited similar behavior in the past are likely to have similar preferences in the future. It identifies patterns and similarities between users and items to make recommendations. For example, if user A and user B both like similar books, and user A reads a new book, the system can recommend that book to user B.

[0056] System 250 can use content-based filtering to determine the attributes of items and the user's past preferences. System 250 can recommend items similar to those the user has previously shown interest in. For example, if the user frequently watches science fiction (SCI) movies, the system will recommend new SCI movies by analyzing the characteristics of previously liked films.

[0057] System 250 can leverage the strengths of each approach using a hybrid method that combines collaborative and content-based approaches. For example, System 250 can adjust recommendations based on broader data to provide more accurate suggestions. These recommendations are then presented to the user through an interface, typically prioritized or personalized based on the most relevant or popular items. The system continuously updates its model with new data to improve the accuracy and relevance of future recommendations, ensuring that suggestions evolve with the user's changing preferences and behaviors.

[0058] Figure 3 Schematic components of a system for seeding artificial intelligence models, according to one or more embodiments, are shown. For example, Figure 3 A schematic component can be shown for using embeddings of an artificial intelligence model to determine interpretable network routing decisions. Additionally or alternatively, Figure 3 A schematic component can be shown for use in AI models that require interpretability (e.g., for user recommendations, fraud detection, marketing success determination, etc.). Figure 3 As shown, system 300 may include mobile device 322 and user terminal 324. Although in Figure 3 The mobile device 322 and the user terminal 324 are shown as a smartphone and a personal computer, respectively. However, it should be noted that the mobile device 322 and the user terminal 324 can be any computing device, including but not limited to laptop computers, tablet computers, handheld computers and other computer devices (e.g., servers), including “smart”, wireless, wearable and / or mobile devices. Figure 3It also includes cloud component 310. Alternatively, cloud component 310 can be any computing device as described above and can include any type of mobile terminal, fixed terminal, or other device. For example, cloud component 310 can be implemented as a cloud computing system and can be characterized by one or more component devices. It should also be noted that system 300 is not limited to three devices. For example, a user can utilize one or more devices to interact with each other, with one or more servers, or with other components of system 300. It should be noted that although one or more operations are described herein as being performed by a specific component of system 300, in some embodiments, these operations can be performed by other components of system 300. As an example, although one or more operations are described herein as being performed by a component of mobile device 322, in some embodiments, these operations can be performed by a component of cloud component 310. In some embodiments, the various computers and systems described herein can include one or more computing devices programmed to perform the described functions. Additionally or alternatively, multiple users can interact with system 300 and / or one or more components of system 300. For example, in one embodiment, a first user and a second user can interact with system 300 using two different components.

[0059] Regarding the components of mobile device 322, user terminal 324, and cloud component 310, each of these devices can receive content and data via an input / output (hereinafter referred to as "I / O") path. Each of these devices may also include a processor and / or control circuitry to send and receive commands, requests, and other suitable data using the I / O path. The control circuitry may include any suitable processing, storage, and / or input / output circuitry. Each of these devices may also include a user input interface and / or user output interface (e.g., a display) for receiving and displaying data. For example, as... Figure 3 As shown, both the mobile device 322 and the user terminal 324 include a display on which data (e.g., dialogue responses, queries, and / or notifications) are displayed.

[0060] Additionally, since mobile device 322 and user terminal 324 are shown as touchscreen smartphones, these displays also serve as user input interfaces. It should be noted that in some embodiments, the device may have neither a user input interface nor a display, and may alternatively use another device (e.g., a dedicated display device, such as a computer screen, and / or a dedicated input device, such as a remote control, mouse, voice input, etc.) to receive and display content. Furthermore, the device in system 300 may run an application (or another suitable program). The application may cause the processor and / or control circuitry to perform operations related to generating dynamic dialogue responses, queries, and / or notifications.

[0061] Each of these devices may also include an electronic storage device. An electronic storage device may include a non-transitory storage medium that electronically stores information. The electronic storage medium of an electronic storage device may include one or both of the following: (i) a system storage device that is integrated with a server or client device (e.g., substantially non-removable), or (ii) a removable storage device that is removably connectable to a server or client device via, for example, a port (e.g., a USB port, a FireWire port, etc.) or a drive (e.g., a disk drive, etc.). An electronic storage device may include one or more of the following: optically readable storage media (e.g., optical discs, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard disk drives, floppy disk drives, etc.), charge-based storage media (e.g., EEPROM, RAM, etc.), solid-state storage media (e.g., flash drives, etc.), and / or other electronically readable storage media. An electronic storage device may include one or more virtual storage resources (e.g., cloud storage, virtual private networks, and / or other virtual storage resources). An electronic storage device may store software algorithms, information determined by a processor, information obtained from a server, information obtained from a client device, or other information that implements the functions described herein.

[0062] Figure 3 This also includes communication paths 328, 330, and 332. Communication paths 328, 330, and 332 may include the Internet, mobile phone networks, mobile voice or data networks (e.g., 5G or LTE networks), cable networks, public switched telephone networks, or other types of communication networks or combinations thereof. Communication paths 328, 330, and 332 may individually or together include one or more communication paths, such as satellite paths, fiber optic paths, cable paths, paths supporting Internet communications (e.g., IPTV), free-space connections (e.g., for broadcasting or other wireless signals), or any other suitable wired or wireless communication paths or combinations thereof. The computing device may include additional communication paths linking multiple hardware, software, and / or firmware components that operate together. For example, the computing device may be implemented by a cloud, a computing platform operating as the computing device.

[0063] Cloud component 310 may include model 302, which may be a machine learning model, an artificial intelligence model, etc. (collectively referred to herein as "model"). In recent years, the use of artificial intelligence has grown exponentially, including but not limited to machine learning, deep learning, etc. (collectively referred to herein as artificial intelligence model, machine learning model, or simply model). Broadly speaking, artificial intelligence refers to a wide branch of computer science that involves building intelligent machines capable of performing tasks that typically require human intelligence. A key benefit of artificial intelligence is its ability to process data, find underlying patterns, and / or perform real-time determination. However, despite these benefits and despite a wide range of potential applications, the practical implementation of artificial intelligence is hampered by several technical challenges. First, artificial intelligence may rely on large amounts of high-quality data. The process of acquiring this data and ensuring its high quality can be complex and time-consuming. Additionally, the acquired data may need to be accurately classified and labeled, which can be a difficult, time-consuming, and manual task. Second, despite the mainstream popularity of artificial intelligence, its practical implementation may require expertise in designing, programming, and integrating AI-based solutions, which may limit the number of personnel and resources available to create such implementations. Finally, AI-based results may be difficult to audit because the processes that produce those results may be unknown or obscure. This ambiguity may hinder the identification of errors and the improvement of the models that provide the results.

[0064] Model 302 can take input 304 and provide output 306. The input may include multiple datasets, such as training datasets and test datasets. Each of the multiple datasets (e.g., input 304) may include a subset of data related to user data, predicted forecasts and / or errors, and / or actual forecasts and / or errors. In some embodiments, output 306 may be fed back to model 302 as input to train model 302 (e.g., alone or in combination with user indications of the accuracy of output 306, labels associated with the input, or other reference feedback information). For example, the system may receive a first labeled feature input, wherein the first labeled feature input is labeled with a known prediction for the first labeled feature input. The system may then train a first machine learning model to classify the first labeled feature input using the known prediction (e.g., embeddings, labeled embeddings, mappings, corresponding human-defined descriptions, etc.).

[0065] In various embodiments, model 302 can update its configuration (e.g., weights, biases, or other parameters) based on an evaluation of its predictions (e.g., output 306) and reference feedback information (e.g., user indications of accuracy, reference labels, or other information). In various embodiments where model 302 is a neural network, connection weights can be adjusted to reconcile discrepancies between the neural network's predictions and reference feedback. In further use cases, one or more neurons (or nodes) of the neural network may need to backpropagate their respective errors through the neural network to facilitate the update process (e.g., error backpropagation). For example, updates to connection weights could reflect the magnitude of the error backpropagated after the forward pass is complete. In this way, for example, model 302 can be trained to generate better predictions.

[0066] In some embodiments, model 302 may include an artificial neural network. In such embodiments, model 302 may include an input layer and one or more hidden layers. Each neuron of model 302 may be connected to many other neurons of model 302. Such connections may be enforced or inhibited in terms of their influence on the activation state of the connected neurons. In some embodiments, each individual neuron may have a summation function that combines the values ​​of all its inputs. In some embodiments, each connection (or the neuron itself) may have a threshold function such that a signal must exceed it before propagating to other neurons. Model 302 may be self-learning and trained rather than explicitly programmed, and may perform significantly better in certain domains of problem-solving compared to conventional computer programs. During training, the output layer of model 302 may correspond to a classification of model 302, and inputs known to correspond to that classification may be input into the input layer of model 302 during training. During testing, inputs without a known classification may be input into the input layer, and the determined classification may be output.

[0067] In some embodiments, model 302 may include multiple layers (e.g., where signal paths traverse from front to back layers). In some embodiments, model 302 may utilize backpropagation techniques, where forward stimulation is used to reset weights on “pre-” neurons. In some embodiments, stimulation and inhibition in model 302 may be more free-flowing, where connections interact in a more chaotic and complex manner. During testing, the output layer of model 302 may indicate whether a given input corresponds to a classification of model 302 (e.g., embedding, labeled embedding, mapping, corresponding human-defined description, etc.).

[0068] In some embodiments, the model (e.g., model 302) may automatically perform actions based on output 306. In some embodiments, the model (e.g., model 302) may not perform any actions. The output of the model (e.g., model 302) may be used to determine embeddings, tagged embeddings, mappings, corresponding human-defined descriptions, etc.

[0069] In some embodiments, the system can generate predictions related to financial services. For example, the system may use one or more models and / or applications to process various data to generate predictions for tasks such as payment card eligibility determination, fraud detection, and / or determining rates for auto finance applications. For credit card eligibility, the model may use data such as the applicant's credit score, income, employment history, debt-to-income ratio, and past credit history. This data helps the model predict the likelihood of the applicant repaying credit card debt. For fraud detection, the model analyzes transaction data, including the amount, location, frequency, and patterns of transactions. They compare these patterns with known fraudulent activities to identify potential fraudulent activity. To determine auto finance rates, the model may use the applicant's credit score, loan amount, loan term, vehicle details, and market interest rates. The data used by these models comes from various sources, including credit bureaus, financial institutions, customer-provided information, transaction records, and public records. By analyzing these data points, the model can make informed predictions and decisions, helping financial institutions manage risk, provide appropriate services, and improve customer satisfaction.

[0070] In some embodiments, the model can process the received data through several stages. For example, the model can collect and aggregate data from various sources, such as user accounts, industry data, third-party data sources, etc. The system can ensure that the data is cleaned and preprocessed to handle any missing and / or inconsistent information. This preprocessing may include normalizing numerical data, encoding categorical variables, and applying techniques to handle outliers. The model can then use feature engineering to identify and create relevant features that can improve its predictive power. For example, the system can derive new variables from existing variables, such as calculating the debt-to-income ratio from debt and income data.

[0071] Once the data is ready, the system feeds it into a model, which can be an artificial intelligence algorithm such as logistic regression, decision trees, and / or neural networks. The model can be trained on historical data, learning patterns, and / or the relationship between input features and target outcomes. During this training process, the system can adjust model parameters to minimize prediction error. After training, the system can use a separate dataset to validate and test the model to ensure it has predetermined and / or threshold accuracy and generalizability.

[0072] In some embodiments, the system may use task-specific predictions. Additionally or alternatively, the system may tailor its inputs and / or outputs based on the desired determinations and / or predictions. For example, for credit card eligibility, the model may assess the likelihood of an applicant defaulting on payments. In fraud detection, the model may identify anomalies and patterns indicating fraudulent activity. In auto finance interest rate determination, the model may predict the risk associated with lending to individuals and adjust interest rates accordingly. In some embodiments, the entire process may be iterative, with models continuously updated and refined as new data becomes available to ensure they remain effective in making accurate and reliable predictions.

[0073] In some embodiments, model 302 can be a deep learning model. Training a deep learning model to generate recommendations involves several stages, each crucial for ensuring the model's effectiveness and accuracy. Initially, the system collects and preprocesses a large dataset containing user interactions, preferences, ratings, and other relevant features. This data is then split into training, validation, and test sets. The training set is used to teach the model, the validation set helps tune hyperparameters and prevent overfitting, and the test set evaluates the model's performance on unseen data.

[0074] Then, deep learning models (usually neural networks) are designed to suit recommendation tasks. Common architectures include collaborative filtering models, such as matrix factorization that leverages neural networks for enhancement, and content-based models that use user and item features. In some cases, hybrid models combining both approaches are employed. The architecture of such models typically consists of multiple layers, including embedding layers that transform user and item identifiers into dense vectors, and hidden layers that capture complex interactions between users and items.

[0075] During training, the model learns to predict user preferences by minimizing loss functions, such as mean squared error for rating predictions or binary cross-entropy for implicit feedback scenarios. Optimization algorithms such as stochastic gradient descent (SGD) or its variants are used to iteratively update the model's weights. Throughout training, techniques such as dropout and regularization can be applied to prevent overfitting and improve generalization.

[0076] Once trained, the model can generate recommendations by predicting the relevance of items for each user. For a given user, the model processes their historical interactions and preferences to output a ranked list of items they are most likely to be interested in. This involves feeding the user's data into the model, which then calculates scores for all possible items. The item with the highest score is selected as the recommendation.

[0077] The system can further enhance recommendations through techniques such as real-time updates, where the model continuously learns from new user interactions and incorporates additional context, such as temporal dynamics or user intent. Finally, the recommendations are presented to the user through an interface, often accompanied by explanations or personalized messages to enhance user engagement and satisfaction. The entire process ensures that the deep learning model effectively learns from the data to provide accurate and personalized recommendations.

[0078] System 300 also includes an API layer 350. API layer 350 allows the system to generate profiles across different devices. In some embodiments, API layer 350 may be implemented on mobile device 322 or user terminal 324. Alternatively or additionally, API layer 350 may reside on one or more cloud components 310. API layer 350 (which may be a REST or Web services API layer) can provide a decoupled interface to data and / or functionality of one or more applications. API layer 350 can provide a public, language-agnostic way to interact with applications. Web services APIs provide a well-defined contract, called a WSDL, which describes the service in terms of its operations and the types of information used to exchange information. REST APIs typically do not have this contract; instead, they are documented with client libraries in most common languages, including Ruby, Java, PHP, and JavaScript. Traditionally, SOAP Web services have been used in enterprises to publish internal services and to exchange information with partners in B2B transactions.

[0079] API layer 350 can be deployed in various architectural ways. For example, system 300 can be partially based on API layer 350, enabling extensive use of SOAP and RESTful web services, utilizing resources such as service repositories and developer portals, while offering low management, standardization, and separation of concerns. Alternatively, system 300 can be entirely based on API layer 350, allowing for separation of concerns between layers such as API layer 350, services, and applications.

[0080] In some embodiments, the system architecture can use a microservices approach. Such systems can use two types of layers: a front-end layer and a back-end layer where microservices reside. In this architecture, the role of API layer 350 is to provide integration between the front-end and back-end. In this case, API layer 350 can use a RESTful API (exposing communication between the front-end or even microservices). API layer 350 can use AMQP (e.g., Kafka, RabbitMQ, etc.). API layer 350 can also utilize newer communication protocols such as gRPC and Thrift for initial use.

[0081] In some embodiments, the system architecture may use an open API approach. In this case, API layer 350 may use commercial or open-source API platforms and their modules. API layer 350 may use a developer portal. API layer 350 may use strong security constraints with WAF and DDoS protection applied, and API layer 350 may use RESTful APIs as the standard for external integration.

[0082] Figure 4 A flowchart illustrating the steps involved in using a seeded artificial intelligence model according to one or more embodiments is shown. For example, a system may use process 400 (e.g., as implemented on one or more system components described above) to determine interpretable network routing decisions using embeddings of the artificial intelligence model. In another example, process 400 may describe using non-natural language embeddings to generate natural language output with improved accuracy using a classification seed. For example, process 400 may describe a system determining a proxy set of embeddings (e.g., seeds) to seed the artificial intelligence model to generate recommendations for (e.g., previously uninterpretable) a first set of embeddings. The system can use the proxy set of embeddings (e.g., seeds) to generate a first, more interpretable, and accurate output from the seeded model.

[0083] At step 402, process 400 (e.g., using one or more of the components described above) can initiate a session. For example, the system can initiate a first session between a user and a first application, where the first session includes initial input. The system can initiate a session, such as a user or device session, by establishing a connection between an entity (e.g., a user, application, or device) and a platform or service. This process can begin when a user interacts with an application, such as logging in, sending a request, or performing an action that triggers the system to create a session instance. The session is assigned a unique identifier by the system and may include metadata such as timestamps, authentication tokens, user credentials, or device-specific information to track interactions. In the case of a user session, the system can authenticate the user via credentials, biometrics, or multi-factor authentication before granting access. Similarly, a device session may involve verifying device identity, network permissions, or security protocols. Throughout the session, the system maintains state information, allowing for seamless interaction and continuity. The session continues until termination conditions are met, such as the user logging out, session timeout, or explicit session closure by the system. By effectively managing sessions, the system ensures a secure, efficient, and personalized user experience while maintaining security and resource optimization.

[0084] At step 404, process 400 (e.g., using one or more of the components described above) generates a seed. For example, the system may generate a seed based on user profile data aggregated prior to initiating the first session. The system can generate a seed for the model by collecting and processing relevant data that serves as the basis for initial training or adaptation of the model. This seed can be derived from aggregated user profile data, historical interactions, or other contextual information prior to initiating the session. The system may first collect data points, such as user preferences, behavioral patterns, demographic information, or past engagement history, to ensure the seed reflects meaningful insights. Once the data is collected, it is preprocessed—cleaned, structured, and transformed into a format suitable for training the AI ​​model. The system then uses the seed data to initialize the model's parameters, thereby influencing its ability to make accurate predictions or personalized recommendations from the outset. By utilizing a well-formed seed, the AI ​​model can generate more relevant outputs, reducing the need for large-scale retraining and improving its performance from the initial session.

[0085] In some embodiments, the system may receive user profile data of a user interacting with a first computer terminal. The system receives user profile data, network profile data, and / or other profile data of the user interacting with the first computer terminal or data processing requests across a computer network through various mechanisms designed to capture and transmit relevant information. For example, when a user interacts with a computer terminal, such as logging into a service, browsing a website, or using an application, the system collects profile data, which may include personal information, preferences, usage patterns, and behaviors. This data is typically collected through forms, cookies, tracking scripts, and user input and stored in a database associated with the service or application. In addition to user profile data, network profile data is also collected to understand the context of user interactions. This includes information about the user's device, IP address, network conditions, geographic location, and other metadata related to network connectivity. When a user initiates a data processing request (such as submitting a query, downloading a file, or accessing a cloud service), the system captures this network profile data through network monitoring tools, logging mechanisms, and API interactions. The collected data is sent over the network to a central server or cloud-based system, where it is processed and analyzed. Secure transmission protocols (such as HTTPS) are typically used to ensure the privacy and security of the data during transmission. Once the data arrives at the server, it is typically aggregated, stored in a structured format, and analyzed using various data processing techniques. This analysis can include machine learning algorithms, statistical methods, and pattern recognition techniques to gain insights, personalize user experiences, optimize network performance, and enhance security measures.

[0086] In some embodiments, the system can generate a seed based on a set of labeled embeddings. The system can acquire a labeled dataset where each data point is represented by an embedding vector and associated with a label. These embeddings are typically pre-computed representations of the data, capturing semantic information in a dense, continuous vector space. To prepare the model's input, the system first organizes the embeddings and their corresponding labels into a suitable format. This typically involves creating two separate structures: a feature matrix X and a label vector y. The feature matrix X is an m × d matrix, where m is the number of data points and d is the dimension of the embeddings. Each row in X corresponds to an embedding vector, while the label vector y is an m-dimensional vector containing the label for each data point. The system ensures that the data is properly shuffled and split into training, validation, and test sets to evaluate the model's performance. Shuffling helps ensure that the model learns generalizable patterns rather than overfitting to a specific order of data points. The training set is used to train the model, the validation set helps tune hyperparameters and prevent overfitting, and the test set is used to evaluate the model's performance on unseen data.

[0087] Once the data is organized, the system feeds the feature matrix X and label vector y into the model. If the model is a neural network, the embeddings can be fed directly into the input layer. The model processes these embeddings through its architecture, which may include multiple hidden layers, activation functions, and dropout layers, to learn patterns and make predictions. During training, the model iteratively updates its weights using an optimization algorithm such as stochastic gradient descent (SGD) to minimize a loss function, such as classification cross-entropy for classification tasks or mean squared error for regression tasks. After training, the model uses the learned patterns to make predictions about new, unseen embeddings. The system can take a new embedding vector, pass it through the trained model, and obtain a prediction, which can be a class label, a score, or any other task-dependent relevant output. This process ensures that the model effectively utilizes the set of labeled embeddings to learn and make accurate predictions.

[0088] In some embodiments, the system may determine a first size for a first set of embeddings, which refers to the dimension or number of features in each embedding vector within that set. This size is typically influenced by the nature of the data and the embedding method used, such as the dimension chosen in Word2Vec or the output size of an autoencoder. Once the first size is established, the system proceeds to determine a second size for a second set of embeddings. This second size typically depends on the first size to maintain compatibility and consistency between the two sets. The system considers several factors when determining this second size, including data complexity, specific task requirements, and the need to balance performance and computational efficiency. If the second set of embeddings is intended to represent the same type of data but in a different context or after further transformation, the system may choose to maintain the same dimensions as the first set to ensure direct mapping or alignment.

[0089] The system can adjust the size of the second set of embeddings accordingly if it is used for a different purpose or needs to capture more or less information. For example, if the second set needs to represent a higher level of abstraction or reduce dimensionality for efficiency, the system can choose a smaller size. Conversely, if the second set needs to capture more detailed information or additional features, the system may increase the size. The final decision on the second size is to optimize the model's ability to perform its intended function, whether it involves prediction, classification, or other tasks. For example, the system may choose a large number of human / defined / crafted embeddings. The system may need to accumulate a threshold amount of data for various categories. The system may need values ​​for users, activities, etc. The more engineered features there are, the higher the likelihood that the mapping will map one of the engineered features to the embedding (and do so correctly). For example, the system may choose the number and / or size of labeled embeddings based on the amount (or size) of profile data, user input, etc.

[0090] In some embodiments, a labeled set of embeddings is generated by retrieving a first artificial intelligence model. The first artificial intelligence model may be a deep learning model. The deep learning model is trained on a first training dataset to generate the first set of embeddings, wherein the first training dataset is a training dataset. The training dataset may include historical user data generated based on monitoring data processing requests across multiple computer networks. The first training dataset may include historical network traffic data generated based on monitoring data processing requests across multiple computer networks.

[0091] In some embodiments, the system may retrieve a second set of embeddings, where each embedding in the second set is labeled to correspond to one or more corresponding human-defined descriptions. For example, one or more corresponding human-defined descriptions might correspond to known network problems, known user profile data categories, etc. For instance, the system assigns labels to each embedding based on human-defined descriptions. This labeling process involves mapping each embedding to one or more descriptions that are meaningful and human-interpretable. For example, embeddings representing network problems could be labeled with categories such as “latency issues,” “connection interruptions,” or “high traffic,” while embeddings representing user profile data could be labeled with categories such as “frequent travelers,” “tech enthusiasts,” or “online shoppers.” This mapping can be done manually by domain experts or, if a predefined set of descriptions exists, through an automated classification algorithm.

[0092] The labeled embeddings are then organized into a structured dataset, where each embedding vector is associated with one of its corresponding human-defined descriptions (multiple). This dataset can be used for various downstream tasks, such as training machine learning models, clustering, or anomaly detection. The labels provide interpretability to the embeddings, enabling users to understand the underlying patterns and relationships captured by the embeddings based on familiar and meaningful categories. As new data becomes available, the system continuously updates and refines the embeddings and their labels. This ensures that the embeddings remain relevant and accurate, reflecting any changes or new insights in the data. By generating a collection of labeled embeddings, the system creates a powerful tool for analyzing complex data and making it accessible and interpretable through human-defined descriptions.

[0093] In some embodiments, the system can map a first set of embeddings to a second set of embeddings. For example, the system can use a mapping function that transforms the first set of embeddings into the second set. If a labeled dataset exists that provides a correspondence between the two sets of embeddings, this mapping can be learned through supervised learning methods. For example, suppose the first set of embeddings represents raw user interactions, and the second set represents predefined user categories, such as "frequent travelers" or "tech enthusiasts." In this case, the system can use a labeled dataset where each user interaction embedding is associated with one or more user category embeddings. The system can train a neural network or another machine learning model on this labeled dataset to learn the mapping function. The model takes embeddings from the first set as input and outputs embeddings from the second set. During training, the model minimizes a loss function that measures the difference between the predicted second set of embeddings and the actual embeddings, thereby learning to appropriately transform the input. Once trained, the model can efficiently map new embeddings from the first set to the second set. This involves feeding new embeddings into the trained model, which processes them through its learned layers to output the corresponding second set of embeddings. The result is a set of embeddings of transformations aligned with the desired features or task, thereby providing a meaningful and interpretable representation in the new space.

[0094] In some embodiments, the system may label the first set of embeddings based on mapping a first set of embeddings to a second set of embeddings to generate a labeled set of embeddings. The system can use the mapping to transform new embeddings from the first set into corresponding embeddings in the second set. These transformed embeddings are then compared to the pre-labeled second set of embeddings. The system typically uses a similarity metric, such as cosine similarity or Euclidean distance, to identify the closest matching embeddings from the second set. After identifying the closest matches, the system assigns corresponding labels from the second set to the first set of embeddings. This process leverages one or more human-defined descriptions derived from the second set to efficiently label each embedding from the first set. The result is a labeled set of embeddings, where each embedding from the first set is annotated with a meaningful category or feature from the second set. This labeling process allows the system to make the first set of embeddings interpretable and actionable by associating it with known categories or features. It is particularly useful in scenarios where the initial embeddings are abstract or complex, and the labeled set of embeddings provides clearer insights and facilitates further analysis or decision-making based on the labeled descriptions.

[0095] In some embodiments, mapping a first set of embeddings to a second set of embeddings may include the system receiving an objective function and updating the coefficients of the first set of embeddings to minimize the objective function. For example, the system may begin by defining an objective function that quantifies the difference or error between the mapped embeddings and the target embeddings in the second set. This objective function may be based on various criteria, such as mean squared error, cross-entropy, or a more specialized loss function designed for the specific task at hand. Once the objective function is established, the system initializes the coefficients to be applied to the first set of embeddings. These coefficients may be weights in a neural network or parameters in a simpler linear transformation. The system then computes an initial mapping from the first set of embeddings to the second set by applying the current coefficients. At the heart of this process is an optimization loop, where the system iteratively updates the coefficients to reduce the value of the objective function. This is typically done using gradient-based optimization methods such as stochastic gradient descent (SGD), Adam, or other variations. During each iteration, the system computes the gradient of the objective function relative to the coefficients, which indicates the direction in which the coefficients should be adjusted to reduce the error. The system updates the coefficients by moving them in the direction that reduces the objective function, thereby effectively improving the alignment between the first and second sets of embeddings. This process continues iteratively, with the coefficients adjusted after each iteration of the data, gradually refining the mapping. As optimization proceeds, the objective function value decreases, indicating that the first set of embeddings is increasingly transformed in a way that closely aligns with the second set. The process ends once the system reaches a satisfactory minimization level—typically determined by a convergence criterion or a predefined number of iterations. The resulting coefficients now represent the optimal mapping function that can be applied to new embeddings from the first set to efficiently map them to the second set.

[0096] In some embodiments, mapping a first set of embeddings to a second set of embeddings may include the system retrieving a lasso regression model and minimizing the residual sum of squares (RSS) between the coefficients of the first and second sets of embeddings using a linear approximation. For example, the system may retrieve or construct a lasso regression model designed to find a linear relationship between the first set of embeddings (as input features) and the second set of embeddings (as the target output). In this case, the first set of embeddings is used as the independent variable, and the second set of embeddings is used as the dependent variable that the system aims to predict or approximate. The lasso regression model introduces a linear approximation by assuming that the second set of embeddings can be represented as a linear combination of the first set of embeddings adjusted by the set of coefficients. The goal is to determine these coefficients in a way that minimizes the difference between the predicted embeddings and the actual embeddings in the second set. To achieve this, the system defines a loss function based on the residual sum of squares (RSS), which measures the total squared difference between the predicted and actual embeddings in the second set. The lasso regression model attempts to minimize this RSS while also applying an L1 regularization penalty to the coefficients. This regularization term encourages sparsity in the coefficients, meaning it forces many of them to be zero, effectively selecting only the most relevant features from the first set that contribute to the approximation of the second set. The system then iteratively adjusts the coefficients using optimization techniques such as coordinate descent. During each iteration, the model updates the coefficients in a manner that reduces the RSS while balancing the regularization penalty. As optimization progresses, the coefficients converge to values ​​that provide the best linear approximation of the second set of embeddings from the first set, subject to sparsity constraints imposed by L1 regularization. Once optimization is complete, the resulting lasso regression model provides a set of coefficients that map the first set of embeddings to the second set of embeddings. These coefficients not only create a linear approximation of the target embeddings but also highlight which features in the first set are most important, as many coefficients might be zeroed out due to lasso's feature selection capabilities.

[0097] In some embodiments, mapping a first set of embeddings to a second set of embeddings may include a system determining coefficients for the first and second sets of embeddings and minimizing the difference between the coefficients. The system may determine a set of coefficients that defines the relationship between the first and second sets of embeddings. These coefficients represent the weight or importance of each dimension in the embeddings used to predict or approximate the target embeddings in the second set. The system defines a loss function that quantifies the difference between the coefficients of the first set of embeddings and the coefficients required to generate the second set of embeddings. Depending on the specific requirements of the task, various metrics, such as Euclidean distance or another norm, may be used to measure this difference. The goal of the system is to minimize this difference, effectively making the first set of embeddings as similar as possible to the second set in terms of the linear relationship defined by the coefficients. To minimize this difference, the system employs an optimization algorithm, such as gradient descent, which iteratively adjusts the coefficients of the first set of embeddings. During each iteration, the system computes the gradient of the loss function with respect to the coefficients and updates them in the direction that reduces the difference between the coefficients of the first and second sets. As the optimization process continues, the system refines the coefficients to minimize the loss function, thereby improving the alignment between the two sets of embeddings. The result is an optimized set of coefficients that maps the first set of embeddings to the second set, effectively capturing the relationship between them.

[0098] In some embodiments, the system can generate a seed by determining a first embedding based on a first input (e.g., received during a first session). The system can then determine a second embedding based on user profile data, where the user profile data is aggregated before initiating the first session. The system can then generate a first session seed based on the first embedding and a second session seed based on the second embedding. The system can then use the first and second session seeds to seed an artificial intelligence model. For example, the system can generate a seed for the artificial intelligence model by utilizing embeddings derived from real-time user input and pre-aggregated user profile data. When the first session is initiated, the system processes the first input—such as a user action, query, or interaction with the application—and determines the first embedding. This embedding represents a mathematical vectorization of the input, capturing its semantic meaning and contextual relevance. Simultaneously, the system retrieves and processes pre-aggregated user profile data, which may include historical interactions, preferences, and behavioral patterns, to generate a second embedding encapsulates long-term user tendencies and attributes.

[0099] Once the two embeddings are computed, the system generates a first session seed based on the first embedding and a second session seed based on the second embedding. These seeds serve as initial conditions influencing how the AI ​​model interprets and processes information within a session. By combining the first and second session seeds, the system seeds the AI ​​model, allowing it to generate responses, predictions, or recommendations that are both relevant to the immediate session context and aligned with broader user characteristics. This dual-seed approach enhances the adaptability of the AI, enabling it to deliver more personalized and intelligent results while ensuring continuity across multiple user interactions.

[0100] In step 406, process 400 (e.g., using one or more of the components described above) seedes the AI ​​model. For example, the system can seed a first AI model to generate a seeded AI model. The system can seed the AI ​​model based on user profile data by embedding specific initial state or contextual information to shape how the model interprets and generates responses. This process begins with the system aggregating user profile data—such as past interactions, preferences, and behavioral trends—and converting it into structured embeddings that encapsulate semantic meaning. Instead of using this embedding as direct input to replace raw text or other forms of user interaction, the system integrates it into a policy layer within the model workflow. This allows the AI ​​model to leverage the semantic richness of the preprocessed user data while still dynamically processing real-time input.

[0101] By incorporating this seed into the model's initialization or hidden state representation, the model can adjust its weights, biases, or attention mechanisms to align with the user's profile before receiving any new input. This approach enhances personalization by ensuring that the model's output not only responds to immediate user actions but also draws upon broader contextual knowledge. This approach is particularly valuable in applications such as dialogue models, recommendation engines, and adaptive learning systems, where balancing real-time interaction with long-term personalization leads to more coherent, relevant, and user-specific responses.

[0102] In some embodiments, the system can align the model's weights, biases, and / or attention mechanisms with the user's profile by utilizing a seed as an initialization parameter within the model architecture before receiving any new input. This process begins by encoding user profile data into structured embeddings that capture key behavioral patterns, preferences, and contextual information. The system then integrates the seed into the model's neural network by modifying its initial hidden states, preprocessing activation functions, and / or influencing the attention distribution among input features. In transformer-based models, for example, the seed can be used to bias the attention mechanism by assigning higher relevance to certain lemma relations based on previous user interactions. In deep learning models, the seed can initialize specific weight configurations to ensure that early activations reflect personalized preferences, rather than starting from neutral or random states. By preloading a model with this context-awareness, the system can generate more relevant, coherent, and personalized outputs from the first user interaction, thereby enhancing responsiveness without compromising real-time adaptability.

[0103] In some embodiments, the system can use a category name for each seed, which helps prevent the model from having to decipher information from different categories. When embeddings are categorized, the model can easily identify and process information based on these predefined labels. This categorization acts as a guiding framework, ensuring that the model can quickly and accurately align the input embeddings with the appropriate context and semantic information needed to generate the output. It minimizes the risk of cross-category confusion, which the model might otherwise struggle to separate and correctly interpret overlapping or intertwined information from various categories. By clearly defining categories, the model can maintain a clear and organized representation of the input data, resulting in more coherent and context-aware output.

[0104] The improved interpretability and categorization of structured embeddings help seed models by providing a more transparent, organized, and meaningful foundation for initializing AI systems. When embeddings are better categorized—such as grouping them based on user behavior patterns, preference clustering, or domain-specific context—the model can more effectively utilize relevant information while minimizing noise from less relevant data. Enhanced interpretability ensures that embeddings retain human-understandable properties, making it easier to diagnose biases, optimize seed selection, and improve the alignment of AI with user expectations. By structuring embeddings into distinct categories, the system can prioritize certain features over others when adjusting weights, biases, and attention mechanisms, resulting in more accurate and context-rich initialization. Furthermore, well-categorized embeddings allow for modular seeding, where different aspects of the user profile—such as interests, historical interactions, and inferred intent—can be selectively integrated based on application needs.

[0105] At step 408, process 400 (e.g., using one or more of the components described above) feeds a first input into the seeded AI model. For example, the system may feed the first input into the seeded AI model to generate a first output. For example, the model processes the input according to its architecture, whether it is a neural network, decision tree, or other type of machine learning model, to generate the output. This output can be a prediction, classification, recommendation, or any other type of result, depending on the model's purpose.

[0106] In some embodiments, the system can feed a first input into a seeded AI model to generate a first output by: generating a first dataset based on the first input; generating a second dataset based on a seed; appending the first dataset to the second dataset to generate a third dataset; and feeding the third dataset into the seeded AI model. Based on the first input, the system generates the first dataset, which is typically a structured representation of the input, such as a table, matrix, or set of embeddings, depending on the nature of the data and the requirements of the model. Similarly, the system receives a seed, which may contain additional data, contextual information, or supplementary features. The system processes the seed to generate the second dataset, which is formatted in a manner aligned with the structure of the first dataset. Both datasets are carefully considered and prepared to ensure compatibility when combined; this may involve standardizing data formats, ensuring a consistent feature set, and applying any necessary preprocessing steps, such as normalization or embedding generation. Once the first and second datasets are generated, the system appends the first dataset to the second dataset, effectively merging them into a third dataset. This third dataset now contains combined information from the two inputs, organized in a way that allows the second model to process it as a unified set of features. The merging process typically involves concatenating rows or columns, depending on whether the input represents different samples or different features of the same sample. After constructing the third dataset, the system feeds it into the second model. By integrating the first and seed datasets into a single dataset, the system ensures that the second model has access to all relevant information in the merged format, enabling it to make more informed and accurate decisions.

[0107] At step 410, process 400 (e.g., using one or more of the components described above) generates recommendations based on the output of the seeded artificial intelligence model. For example, the system may generate a first recommendation based on a first output for display on a user interface, where the first recommendation indicates the result of an interaction between the user and the first computer terminal. The system processes the first output, which may be the result of an interaction between the user and the computer terminal, network analysis, or another computational task. This output may include data such as user behavior, system performance metrics, or network traffic patterns. The system then interprets the output using predefined rules, algorithms, or machine learning models trained to identify patterns and suggest actions. For example, if the first output indicates a decline in network performance, the system may identify potential causes, such as inefficient routing or hardware problems, and generate network routing recommendations to address the issue. If the output relates to user interactions, such as frequent access to a particular application, the system may recommend shortcuts, customizations, or optimizations to enhance the user experience.

[0108] Once a recommendation is made, the system formats it for display on the user interface. This involves translating the technical details into clear, concise language that users can easily understand. The recommendation may include a brief explanation of the identified problem or opportunity, along with the suggested action or change. For example, the interface might display something like, “We’ve noticed your network is slowing down. Consider rerouting traffic through server B to improve performance” or “You frequently use application X. Would you like to add a shortcut to your desktop?” The system ensures that the recommendation is prominently displayed on the user interface, possibly with visual cues such as icons or warnings to draw attention. Users can then review the recommendation and take the suggested action directly through the interface, or they can be guided through a process of implementing the change.

[0109] In some embodiments, the system can generate a first recommendation for display based on a first output by determining a first rationale for the first recommendation based on a set of labeled embeddings and generating the first rationale for display in the user interface. For example, using insights obtained from a mapping, the system determines the first rationale for the recommendation. This rationale is a clear and concise explanation that links the first output to the recommendation by highlighting key factors or patterns identified through the embeddings. For example, if the recommendation is to reroute network traffic, the rationale could state, "This recommendation is based on a detected latency issue that is related to high traffic on your current network path, as identified by the corresponding embedding." The system then generates the first recommendation, integrating the rationale to ensure the user understands the reasoning behind the suggestion. Both the recommendation and the rationale are formatted for display on the user interface. The rationale is presented alongside the recommendation, providing context and transparency so the user can make an informed decision. For example, the user interface could display, "Recommendation: Reroute traffic through server B. Ratione: Based on recent traffic patterns and latency detected in your current network path, rerouting is expected to improve performance by reducing bottlenecks." This combined display of the recommendation and the rationale not only guides the user to take action but also builds trust by making the system's decision-making process clear and understandable. By linking recommendations to clearly defined reasons derived from tagged embeddings, the system ensures users gain relevant and transparent actionable insights, thereby fostering confidence in system guidance and improving the overall user experience.

[0110] expected Figure 4 The steps or descriptions herein can be used in conjunction with any other embodiment of this disclosure. Additionally, regarding... Figure 4 The described steps and descriptions may be performed in an alternative order or in parallel to facilitate the purposes of this disclosure. For example, each of these steps may be performed in any order, in parallel, or simultaneously to reduce lag or increase the speed of the system or method. Furthermore, it should be noted that any component, device, or apparatus associated with the foregoing figures may be used to perform the procedure. Figure 4 One or more steps in the process.

[0111] The embodiments described above are presented for illustrative purposes and not for limitation, and this disclosure is limited only by the appended claims. Furthermore, it should be noted that the features and limitations described in any embodiment can be applied to any embodiment herein, and flowcharts or examples associated with one embodiment can be combined with any other embodiment in a suitable manner, performed in a different order, or performed in parallel. Additionally, the systems and methods described herein can be performed in real time. It should also be noted that the above systems and / or methods can be applied to other systems and / or methods, or used according to other systems and / or methods.

[0112] The following examples will provide a better understanding of this technology:

[0113] 1. A method for generating natural language output with improved accuracy using non-natural language embeddings and classification seeds.

[0114] 2. The method according to the foregoing embodiments further includes: initiating a first session between a user and a first application, wherein the first session includes a first input; generating a seed based on user profile data aggregated before initiating the first session; seeding a first artificial intelligence model using the seed to generate a seeded artificial intelligence model; inputting the first input into the seeded artificial intelligence model to generate a first output; and generating a first recommendation from the seeded artificial intelligence model based on the first output for display on a user interface.

[0115] 3. The method according to any one of the foregoing embodiments, wherein generating a seed based on user profile data aggregated before initiating the first session further includes: retrieving a first artificial intelligence model, wherein the first artificial intelligence model is a deep learning model, wherein the deep learning model is trained on a first training dataset to generate a first set of embeddings, and wherein the first training dataset includes historical user data generated based on monitoring data processing requests across multiple computer networks; retrieving a second set of embeddings, wherein each embedding in the second set of embeddings is labeled as corresponding to one or more corresponding human-defined descriptions, and wherein the one or more corresponding human-defined descriptions correspond to known user profile data categories; mapping the first set of embeddings to the second set of embeddings; and labeling the first set of embeddings based on mapping the first set of embeddings to the second set of embeddings to generate a labeled set of embeddings.

[0116] 4. The method according to any one of the foregoing embodiments, wherein generating a first recommendation for display based on a first output further includes: determining a first reason for the first recommendation based on the labeled embedding set; and generating the first reason for display in a user interface.

[0117] 5. The method according to any one of the foregoing embodiments, wherein mapping the first embedding set to the second embedding set comprises: receiving an objective function; and updating the coefficients of the first embedding set to minimize the objective function.

[0118] 6. The method according to any one of the foregoing embodiments, wherein mapping the first embedding set to the second embedding set comprises: retrieving a lasso regression model; and using a linear approximation to minimize the residual sum of squares (RSS) between the coefficients of the first embedding set and the second embedding set.

[0119] 7. The method according to any one of the foregoing embodiments, wherein mapping the first embedding set to the second embedding set includes: determining the coefficients of the first embedding set and the second embedding set; and minimizing the difference between the coefficients.

[0120] 8. The method according to any one of the foregoing embodiments, wherein retrieving the second embedding set further includes: determining a first size of the first embedding set; and determining a second size of the second embedding set based on the first size.

[0121] 9. The method according to any one of the foregoing embodiments, wherein retrieving the second embedding set further includes: determining a first size of the user profile data; and determining a second size of the second embedding set based on the first size.

[0122] 10. The method according to any one of the foregoing embodiments, wherein retrieving the second embedding set further includes: receiving user input; and determining the size of the second embedding set based on the user input.

[0123] 11. The method according to any one of the foregoing embodiments, wherein mapping the first embedding set to the second embedding set further comprises: determining a proxy embedding set for the first embedding set; and using the proxy embedding set to generate a first output.

[0124] 12. The method according to any one of the foregoing embodiments, wherein inputting the first input into the seeding artificial intelligence model to generate the first output further includes: generating a first dataset based on the first input; generating a second dataset based on the seed; appending the first dataset to the second dataset to generate a third dataset; and inputting the third dataset into the seeding artificial intelligence model.

[0125] 13. The method according to any one of the foregoing embodiments, wherein generating a first recommendation based on a first output for display on a user interface further includes: retrieving a plurality of recommendations; and selecting a first recommendation from the plurality of recommendations based on the first output.

[0126] 14. The method according to any one of the foregoing embodiments, wherein generating a first recommendation from the seeded artificial intelligence model based on the first output for display on the user interface further includes: retrieving a large language model; and inputting the first output into the large language model.

[0127] 15. The method according to any one of the foregoing embodiments, wherein generating a first recommendation from the seeded artificial intelligence model based on the first output for display on the user interface further comprises: generating a first embedding based on the first output; and determining the first recommendation based on the first embedding.

[0128] 16. One or more non-transitory computer-readable media storing instructions that, when executed by a data processing apparatus, cause the data processing apparatus to perform operations including any one of embodiments 1-15.

[0129] 17. A system comprising: one or more processors; and a memory storing instructions that, when executed by the processor, cause the processor to perform operations including any one of embodiments 1-15.

[0130] 18. A system comprising means for performing any one of embodiments 1-15.

Claims

1. A system for seeding networks using network profiling data, the system comprising: Receive a request for network routing across a first computer network in response to a first data processing request; Determine the network profile data of the first computer network; The first input is generated based on the network profile data; A seed is generated based on a set of labeled embeddings, wherein the set of labeled embeddings is generated through the following operations: Retrieve a first artificial intelligence model, wherein the first artificial intelligence model is a deep learning model for determining network routes, wherein the deep learning model is trained on a first training dataset to generate a first set of embeddings, and wherein the first training dataset includes historical network traffic data generated based on monitoring data processing requests across multiple computer networks; Retrieve a second set of embeddings, wherein each embedding in the second set of embeddings is labeled as corresponding to one or more corresponding human-defined descriptions, wherein the one or more corresponding human-defined descriptions correspond to known network problems; Map the first embedding set to the second embedding set; and The first embedding set is labeled by mapping the first embedding set to the second embedding set to generate a labeled embedding set; The first artificial intelligence model is seeded with the seeds to generate a seeded artificial intelligence model; The first input is fed into the artificial intelligence model of the seeding process to generate a first output; and Based on the first output, a first recommended network route for the first data processing request is generated for display on the user interface.

2. A method for generating natural language output with improved accuracy using non-natural language embeddings and classification seeds, the method comprising: Initiate a first session between the user and the first application, wherein the first session includes a first input; The seed is generated based on user profile data aggregated before the first session is initiated; The seeds are used to sow the first artificial intelligence model to generate a sown artificial intelligence model; The first input is fed into the artificial intelligence model of the seeding process to generate a first output; and Based on the first output, a first recommendation is generated from the seeded artificial intelligence model for display on the user interface.

3. The method according to claim 2, wherein, Generating the seed based on the user profile data aggregated before initiating the first session also includes: Retrieve a first artificial intelligence model, wherein the first artificial intelligence model is a deep learning model, wherein the deep learning model is trained on a first training dataset to generate a first set of embeddings, and wherein the first training dataset includes historical user data generated based on monitoring data processing requests across multiple computer networks. Retrieve a second set of embeddings, wherein each embedding in the second set of embeddings is labeled as corresponding to one or more corresponding human-definable descriptions, and wherein the one or more corresponding human-definable descriptions correspond to known user profile data categories; Map the first embedding set to the second embedding set; and The first embedding set is labeled by mapping the first embedding set to the second embedding set to generate a labeled embedding set.

4. The method according to claim 3, wherein, Generating the first recommendation for display based on the first output also includes: The first reason for the first recommendation is determined based on the labeled set of embeddings; and The first reason is generated for display in the user interface.

5. The method according to claim 3, wherein, Mapping the first set of embeddings to the second set of embeddings includes: Receive the objective function; and Update the coefficients of the first embedding set to minimize the objective function.

6. The method according to claim 3, wherein, Mapping the first set of embeddings to the second set of embeddings includes: Retrieving Lasso regression models; and A linear approximation is used to minimize the residual sum of squares (RSS) between the coefficients of the first embedding set and the second embedding set.

7. The method according to claim 3, wherein, Mapping the first set of embeddings to the second set of embeddings includes: Determine the coefficients of the first embedding set and the second embedding set; and Minimize the difference between the coefficients.

8. The method according to claim 3, wherein, Retrieving the second set of embeddings also includes: Determine the first size of the first embedding set; and The second size of the second embedding set is determined based on the first size.

9. The method according to claim 3, wherein, Retrieving the second set of embeddings also includes: Determine the first size of the user profile data; and The second size of the second embedding set is determined based on the first size.

10. The method according to claim 3, wherein, Retrieving the second set of embeddings also includes: Receive user input; and The size of the second embedding set is determined based on the user input.

11. The method according to claim 3, wherein, Mapping the first set of embeddings to the second set of embeddings further includes: Determine the proxy embedding set of the first embedding set; and The first output is generated using the proxy embedding set.

12. The method according to claim 2, wherein, The process of inputting the first input into the artificial intelligence model for seeding to generate the first output further includes: Generate a first dataset based on the first input; A second dataset is generated based on the aforementioned seed; Append the first dataset to the second dataset to generate the third dataset; and The third dataset is input into the artificial intelligence model for seeding.

13. The method according to claim 2, wherein, Generating the first recommendation based on the first output for display on the user interface further includes: Searching for multiple recommendations; and Based on the first output, the first recommendation is selected from the plurality of recommendations.

14. The method according to claim 2, wherein, Generating the first recommendation from the seeding AI model based on the first output for display on the user interface further includes: Retrieval of large language models; and The first output is input into the large language model.

15. The method according to claim 2, wherein, Generating the first recommendation from the seeding AI model based on the first output for display on the user interface further includes: A first embedding is generated based on the first output; and The first recommendation is determined based on the first embedding.

16. One or more non-transitory computer-readable media, comprising instructions that, when executed by one or more processors, cause operations including: Initiate a first session between a user and a first application, wherein the first session includes a first input, and wherein the first application includes a first artificial intelligence model trained to process labeled natural language input to generate labeled natural language output; In response to initiating the first session: The first embedding is determined based on the first input; The second embedding is determined based on user profile data, wherein the user profile data is aggregated before the first session is initiated; A first session seed is generated based on the first embedding; A second session seed is generated based on the second embedding; The first AI model is seeded using the first session seed and the second session seed to generate a seeded AI model; The first input is fed into the artificial intelligence model of the seeding process to generate a first output; and Based on the first output, a first recommendation is generated from the seeded artificial intelligence model for display on the user interface.

17. One or more non-transitory computer-readable media according to claim 16, wherein, The process of inputting the first input into the artificial intelligence model for seeding to generate the first output further includes: Generate a first dataset based on the first input; A second dataset is generated based on the aforementioned seed; Append the first dataset to the second dataset to generate the third dataset; and The third dataset is input into the artificial intelligence model for seeding.

18. One or more non-transitory computer-readable media according to claim 16, wherein, Generating the first recommendation based on the first output for display on the user interface further includes: Searching for multiple recommendations; and Based on the first output, the first recommendation is selected from the plurality of recommendations.

19. One or more non-transitory computer-readable media according to claim 16, wherein, Generating the first recommendation from the seeding AI model based on the first output for display on the user interface further includes: Retrieval of large language models; and The first output is input into the large language model.

20. One or more non-transitory computer-readable media according to claim 16, wherein, Generating the first recommendation from the seeding AI model based on the first output for display on the user interface further includes: A first embedding is generated based on the first output; and The first recommendation is determined based on the first embedding.

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